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t5-small-science-papers-NIPS

This model is a fine-tuned version of Dagar/t5-small-science-papers on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 4.7566
  • Rouge1: 15.7066
  • Rouge2: 2.5654
  • Rougel: 11.4679
  • Rougelsum: 14.4017
  • Gen Len: 19.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 318 5.1856 13.7172 2.0644 10.2189 12.838 19.0
5.4522 2.0 636 5.0383 15.6211 2.1808 11.3561 14.3054 19.0
5.4522 3.0 954 4.9486 15.1659 2.3308 11.1052 13.9456 19.0
5.1254 4.0 1272 4.8851 15.716 2.4099 11.4954 14.5099 19.0
4.9794 5.0 1590 4.8456 15.5507 2.4267 11.3867 14.3237 19.0
4.9794 6.0 1908 4.8073 15.8406 2.4254 11.6878 14.6154 19.0
4.8823 7.0 2226 4.7872 15.5554 2.4637 11.3401 14.3183 19.0
4.8338 8.0 2544 4.7680 15.4783 2.4888 11.3364 14.2031 19.0
4.8338 9.0 2862 4.7621 15.958 2.5662 11.6139 14.6576 19.0
4.7838 10.0 3180 4.7566 15.7066 2.5654 11.4679 14.4017 19.0

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1+cu113
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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